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What is a Data Pipeline?

Astera

By orchestrating these processes, data pipelines streamline data operations and enhance data quality. Evolution of Data Pipelines: From CPU Automation to Real-Time Flow Data pipelines have evolved over the past four decades, originating from the automation of CPU instructions to the seamless flow of real-time data.

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What is ETL? – Extract, Transform, Load Explained

Astera

Data Quality: ETL facilitates data quality management , crucial for maintaining a high level of data integrity, which, in turn, is foundational for successful analytics and data-driven decision-making. ETL pipelines ensure that the data aligns with predefined business rules and quality standards.

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The Future of AI in Data Warehousing: Trends and Predictions 

Astera

For example, GE Healthcare leverage AI-powered data cleansing tools to improve the quality of data in its electronic medical records, reducing the risk of errors in patient diagnosis and treatment. Continuous Data Quality Monitoring According to Gartner , poor data quality cost enterprises an average of $15 million per year.

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ETL Batch Processing: A Comprehensive Guide

Astera

Batch processing shines when dealing with massive data volumes, while streaming’s real-time analytics, like in fraud detection, prompt immediate action. Data Processing Order Batch processing lacks sequential processing guarantees, which can potentially alter the output sequence.

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ETL Batch Processing: A Comprehensive Guide

Astera

Batch processing shines when dealing with massive data volumes, while streaming’s real-time analytics, like in fraud detection, prompt immediate action. Data Processing Order Batch processing lacks sequential processing guarantees, which can potentially alter the output sequence.

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Data Integration 101: Understanding The Basics

Astera

Data Movement Data movement from source to destination, with minimal transformation. Data movement involves data transformation, cleansing, formatting, and standardization. Data Quality Consideration Emphasis is on data availability rather than extensive data quality checks.

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Data Integration 101: Understanding The Basics

Astera

Data Movement Data movement from source to destination, with minimal transformation. Data movement involves data transformation, cleansing, formatting, and standardization. Data Quality Consideration Emphasis is on data availability rather than extensive data quality checks.